Energy Usage Behavior Research Articles

Clustering and statistical analyses of air-conditioning intensity and use patterns in residential buildings

Energy conservation in residential buildings has gained increased attention due to its large portion of global energy use and potential of energy savings. Occupant behavior has been recognized as a key factor influencing the energy use and load diversity in buildings, therefore more realistic and accurate air-conditioning (AC) operating schedules are imperative for load estimation in equipment design and operation optimization. With the development of sensor technology, it became easier to access an increasing amount of heating/cooling data from thermal energy metering systems in residential buildings, which provides another possible way to understand building energy usage and occupant behaviors. However, except for cooling energy consumption benchmarking, there currently lacks effective and easy approaches to analyze AC usage and provide actionable insights for occupants. To fill this gap, this study proposes clustering analysis to identify AC use patterns of residential buildings, and develops new key performance indicators (KPIs) and data analytics to explore the AC operation characteristics using the long-term metered cooling energy use data, which is of great importance for inhabitants to understand their thermal energy use and save energy cost through adjustment of their AC use behavior. We demonstrate the proposed approaches in a residential district comprising 300 apartments, located in Zhengzhou, China. Main outcomes include: Representative AC use patterns are developed for three room types of residential buildings in the cold climate zone of China, which can be used as more realistic AC schedules to improve accuracy of energy simulation; Distributions of KPIs on household cooling energy usage are established, which can be used for household AC use intensity benchmarking and performance diagnoses.

Linking building energy consumption with occupants’ energy-consuming behaviors in commercial buildings: Non-intrusive occupant load monitoring (NIOLM)

Occupants’ energy-consuming behaviors have a significant influence on overall energy consumption in commercial buildings. Accordingly, understanding and intervening in these behaviors offers a significant opportunity for energy savings in commercial buildings. Current approaches to behavior modification rely on available occupant-specific energy consumption data, but capturing such data is generally expensive. One possible solution to this challenge is to link energy consumption to individual occupants’ energy-use behaviors in commercial buildings. In this context, this study proposes a non-intrusive occupant load monitoring (NIOLM) approach that couples occupancy-sensing data—captured from existing Wi-Fi infrastructures—with power changes in aggregate building-wide energy data to thereby disaggregate building-wide data down to the individual. This paper describes two case studies that investigate the feasibility of using the NIOLM approach to identify occupant-specific energy consumption information. Tracking eleven occupants’ energy-use behaviors using NIOLM over a four-month period resulted in an average F-measure of 0.778 and Accuracy of 0.955. The case studies thereby demonstrated that NIOLM successfully tracks individual occupants’ energy-consuming behaviors at minimal cost by utilizing existing high-resolution metering devices and Wi-Fi network infrastructures in commercial buildings.

Adapting neuroeconomics for environmental and energy policy

AbstractNeuroimaging methods provide insight into the neural mechanisms underlying the decision process, characterizing choice at the individual level and, in a growing number of contexts, predicting national- and market-level behavior. This dual capacity to examine heterogeneity while forecasting aggregate choice is particularly beneficial to those studying environmental decision-making. To effectively reduce residential energy usage and foster other pro-environmental behaviors, policy-makers must understand the effects of information frames and behavioral nudges across individuals who hold a diverse array of attitudes toward the environment and face a broad range of barriers to action. This paper articulates the potential of neuroeconomic methods to aid environmental policy-makers interested in behavior change, especially those interested in closing the energy efficiency gap. Investigation into the roles of affect, eco-labeling and social norms will be discussed, as well as personal identity and climate change beliefs. Combining neuroimaging with behavioral economics experiments can inform the development of effective messaging, characterize the influence of individual differences on the decision process and aid in forecasting the efficacy of policy interventions at scale.

Understanding the recurring patterns of occupants' energy-use behaviors at entry and departure events in office buildings

Office-building occupants' behaviors during their arrivals and departures foreseeably have a large impact on a building's energy consumption since many occupants in control of appliances will turn their devices on or off at these entry and departure events. Consequently, occupants would have various types of energy-use patterns that coincide with their entry and departure events and that repeat over time. Despite the value that knowledge of such patterns would have on better tracking energy-use behaviors, these patterns have not been well explored with empirical data in the literature. Therefore, this paper studies occupants' energy-use behaviors in office buildings to identify and investigate energy-use patterns at entry and departure events. In particular, this research evaluates (1) the delay intervals that manifest between the occupants' entry/departure events and the beginning/end of the occupants' energy-consuming behaviors, and (2) changes in electricity consumption caused by occupants at entry/departure events to identify recurring—and thereby predictable—energy-use patterns associated with individual occupants. In the pursuit of this objective, the energy-use behaviors of 12 occupants in two office buildings were tracked during a four-month period. Results from statistical analyses performed on the collected data reveal that an occupant in an office building typically follows a consistent, recurring delay-interval pattern. In addition, the results show each occupant also follows a recurring pattern of power changes at entry/departure events. By identifying recurring, occupant-specific energy-use behavior patterns, this study significantly contributes to the current body of research and can be used to support research efforts into energy-load disaggregation.

Energy Usage Behavior Modeling in Energy Disaggregation via Hawkes Processes

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household’s energy consumption is user’s daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances by householders across different time slots. The major challenge in modeling such a relationship in that, with ambiguous appliance usage membership of householders, we find it difficult to appropriately model the influence between appliances, since such influence is determined by human behaviors in energy usage. To address this problem, we propose to model the influence between householders’ energy usage behaviors directly through a novel probabilistic model, which combines topic models with the Hawkes processes. The proposed model simultaneously disaggregates the whole home electricity signal into each component appliance and infers the appliance usage membership of household members and enables those two tasks to mutually benefit each other. Experimental results on both synthetic data and four real-world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in not only decomposing the entire consumed energy to each appliance in houses but also the inference of household structures. We further analyze the inferred appliance-householder assignment and the corresponding influence within the appliance usage of each householder and across different householders, which provides insight into appealing human behavior patterns in appliance usage.

Price Discrimination for Energy Trading in Smart Grid: A Game Theoretic Approach

Pricing schemes are an important smart grid feature to affect typical energy usage behavior of energy users (EUs). However, most existing schemes use the assumption that a buyer pays the same price per unit of energy to all suppliers at any particular time when energy is bought. By contrast, here a discriminate pricing technique using game theory is studied. A cake cutting game is investigated, in which participating EUs in a smart community decide on the price per unit of energy to charge a shared facility controller (SFC) in order to sell surplus energy. The focus is to study fairness criteria to maximize sum benefits to EUs and ensure an envy-free energy trading market. A benefit function is designed that leverages generation of discriminate pricing by each EU, according to the amount of surplus energy that an EU trades with the SFC and the EU’s sensitivity to price. It is shown that the game possesses a socially optimal, and hence also Pareto optimal, solution. Further, an algorithm that can be implemented by each EU in a distributed manner to reach the optimal solution is proposed. Numerical case studies are given that demonstrate beneficial properties of the scheme.

A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings

This paper presents a variation focused cluster analysis strategy to identify typical daily heating energy usage profiles of higher education buildings. Different from the existing cluster analysis studies which were primarily developed using Euclidean distance as the dissimilarity measure and tended to group the daily load profiles with similar magnitudes, Partitioning Around Medoids (PAM) clustering algorithm with Pearson Correlation Coefficient-based dissimilarity measure was used in this study to group the daily load profiles on the basis of the variation similarity. A comparison of the proposed strategy with a k-means cluster analysis with Euclidean distance as the dissimilarity measure was also performed. The performance of the proposed strategy was tested and evaluated using the three-year hourly heating energy usage data collected from 19 higher education buildings in Norway. The results demonstrated the effectiveness of the proposed strategy in identifying the typical daily energy usage profiles. The identified typical heating load profiles provided the information such as the peaks and troughs of the daily heating demand, daily high heating demand period and daily load variation. The identified profiles also helped to categorize multiple buildings into different groups in terms of the similar energy usage behaviors to support further energy efficiency initiatives.

A survey on energy consumption and energy usage behavior of households and residential building in urban China

Building performance, equipment efficiency, and occupant behavior play important roles in China urban residential building energy conservation. An online survey was conducted in 2015 to study the urban residential energy and usage behavior. A total of 4964 Chinese urban households participated in the survey, answering questions about their family composition, buildings, energy use and conservation behaviors, and reactions to specific energy conservation policies. The results suggest that the general trend in Chinese urban households is larger unit sizes (on average, 109m2 per household) and smaller families. The average electricity consumption of urban residential buildings is 1690kWh per year per household in 2015 and it continues to grow as home electronics become more widespread and the demand for higher quality of life increases. China urban residential buildings energy use has the following characteristics: steady growth in size and energy consumption of the buildings associated with rapid urbanization, decentralized and individual equipment with diversified energy usage behavior, and relatively low energy consumption level compared to other countries. In addition to the current energy efficiency programs, China should focus on energy consumption and intensities target of building sector, and the key for urban residential building energy efficiency is to retain traditional behaviors and lifestyles, as well as promoting outcome-based energy conservation policies and technology systems to improve indoor environment and comfort.

Smart Grid Testbed for Demand Focused Energy Management in End User Environments

Successful deployment of smart grids necessitates experimental validities of their state-of-the-art designs in two-way communications, real-time demand response and monitoring of consumers' energy usage behavior. The objective is to observe consumers' energy usage pattern and exploit this information to assist the grid in designing incentives, energy management mechanisms, and real-time demand response protocols; so as help the grid achieving lower costs and improve energy supply stability. Further, by feeding the observed information back to the consumers instantaneously, it is also possible to promote energy efficient behavior among the users. To this end, this paper performs a literature survey on smart grid testbeds around the world, and presents the main accomplishments towards realizing a smart grid testbed at the Singapore University of Technology and Design (SUTD). The testbed is able to monitor, analyze and evaluate smart grid communication network design and control mechanisms, and test the suitability of various communications networks for both residential and commercial buildings. The testbeds are deployed within the SUTD student dormitories and the main university campus to monitor and record end-user energy consumption in real-time, which will enable us to design incentives, control algorithms and real-time demand response schemes. The testbed also provides an effective channel to evaluate the needs on communication networks to support various smart grid applications. In addition, our initial results demonstrate that our testbed can provide an effective platform to identify energy wastage, and prompt the needs of a secure communications channel as the energy usage pattern can provide privacy related information on individual user.

Finding faults and influencing consumption: the role of in-home energy feedback displays in managing high-tech homes

In the context of reducing household greenhouse gas emissions, in-home energy feedback displays have been trialled as a mechanism to assist households to monitor and change energy-use behaviour. As we move towards technology-rich zero-energy homes, the challenge of managing energy use and electricity generation systems will increase and a new role for in-home feedback displays may emerge. This paper describes the in-home display and monitoring systems installed in a near-net zero-energy residential estate and provides a summary of the energy-use data generated by the systems. It also draws on 25 in-depth interviews to discuss the residents’ attitudes towards, and experiences interacting with, the in-home feedback display and energy management system. Residents describe how the feedback displays assist them to understand their end-use energy behaviour, reduce net energy use and assess whether household appliances and renewable energy systems are operating correctly. The role of energy system fault identification is highlighted by many interviewees, where the feedback displays provide the means to monitor system performance, identify system failures and maintain low-energy-use outcomes.

New Directions in Measurement and Verification for Performance Contracts

ABSTRACTOver the decades, the determination of energy savings in performance contracting has undergone an evolution from a utility bill based approach towards a more limited exercise, which may sometimes be difficult to relate to actual energy savings in the later years of the contract. These limited measurement and verification (M&V) approaches can reduce the value of performance contracting in the customer's mind, and ultimately hurt the industry. The authors propose a new strategy of determining energy savings, which combines the original utility bill based approach with a retrofit isolation approach and annual re-commissioning. This combined approach should address the customers' need to more easily understand how energy savings are determined, the energy services companies' need to reduce risk from increases in customer energy usage behavior that can decrease measured savings, and should help sustain energy savings over the length of the guaranteed savings period.

Occupant workstation level energy-use prediction in commercial buildings: Developing and assessing a new method to enable targeted energy efficiency programs

As buildings become more energy efficient and automated, the role of occupants becomes more significant to assist buildings in reaching their full energy efficiency potential. Predicting occupants’ consumption behavior has been identified as one of the most challenging processes in energy efficiency programs and building management system operations. Thus, more accurate behavior learning algorithms for commercial buildings are needed to improve energy efficiency. In this paper, we propose a method to predict occupants’ energy-use behavior based on individual energy consumption profiles and assess its potential to increase the effectiveness of energy efficiency programs. The proposed method implements a support vector machine in order to model and predict occupants’ short term energy-use patterns and test hypotheses for the existence of a correlation between occupants’ entropy, efficiency and prediction accuracy. The results show an average accuracy of 83% for individual energy-use pattern prediction while being positively correlated with individuals’ energy-use behavior. The main contributions of this paper are: (1) proposing and validating a new method to predict individuals’ energy-use patterns based on their individual workstation-level energy consumption patterns, and (2) assessing the feasibility and potential of implementing this method to enhance the efficacy of energy efficiency programs to further induce energy efficiency.

Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM)

Studies indicate that occupancy-related energy-use behaviors have a significant influence on overall energy consumption in commercial buildings. In this context, understanding and improving occupants’ energy-consuming behaviors shows promise as a cost-effective approach to decreasing commercial buildings’ energy demands. Current behavior-modification pursuits rely on the data availability of occupant-specific energy consumption, but it is still quite challenging to track occupant-specific energy-consuming behaviors in commercial buildings. On the other hand, individual occupants have unique energy-consumption patterns at their entry and departure events and will typically follow such patterns consistently over time. Thus, analyzing occupants’ energy-use patterns at the time of their entry and departure events plays a critical role in understanding individual occupants’ energy-use behaviors. To this end, this paper aims to develop a non-intrusive occupant load monitoring (NIOLM) approach that profiles individual occupants’ energy-use behaviors at their entry and departure events. The NIOLM approach correlates occupancy-sensing data captured from existing Wi-Fi networks with aggregated building energy-monitoring data in order to disaggregate building energy loads to the level of individual occupants. Results from a 3-month long period of tracking individual occupants validate the feasibility of the NIOLM approach by comparing the framework's outcomes with the individual metering data captured from plug-load sensors. By utilizing existing devices and Wi-Fi network infrastructure, NIOLM provides a new opportunity for current industry and research efforts to track individual occupants’ energy-use behaviors at a minimal cost.

A Comparative Analysis of Energy Usage and Energy Efficiency Behavior in Low- and High-Income Households: The Case of Kitwe, Zambia

Energy efficiency has been an important topic since the latter part of the last century. This is because adoption of energy efficiency measures has been acknowledged as one of the key methods of addressing the negative impact of climate change. In Zambia, however, the need to adopt energy efficiency measures has not just been driven by the imperative to mitigate the negative effects of climate change but also by a critical shortage of energy. This research looks at households’ energy consumption behavior in low- and high-income areas of Kitwe. Recent studies on the relationship between household energy consumption and behavioral lifestyle have been descriptive, with limited emphasis on the relationships between various variables. In this study, descriptive and inferential statistics have been used to investigate relationships between the two income groups and various energy consumption-related variables such as knowledge about energy reduction measures, energy saving strategies, barriers to the use of energy saving strategies, and the motives for using energy reduction strategies. Methodologically, the study was largely quantitative in nature, with questionnaires administered to a combined total of 56 households. However, key interviews were also conducted that helped us to get a clearer understanding of some of the issues covered in the research. Key findings are that whereas the descriptive statistics show that there are behavioral differences between the two income groups, the inferential statistics show that there is no relationship between income level and the energy efficiency variables. This has been found to be consistent with results from studies done elsewhere. The key lesson is that there is low usage of energy efficiency measures in both low- and high-income areas and that the authorities need to change the way information is disseminated to consumers from the current method of advertising to social diffusion.

Why does real-time information reduce energy consumption?

A number of studies have estimated how much energy conservation is achieved by providing households with real-time information on energy use via in-home displays. However, none of these studies tell us why real-time information changes energy-use behavior. We explore the causal mechanisms through which real-time information affects energy consumption by conducting a randomized-control trial with residential households. The experiment attempts to disentangle two competing mechanisms: (i) learning about the energy consumption of various activities, the “learning effect”, versus (ii) having a constant reminder of energy use, the “saliency effect”. We have two main results. First, we find a statistically significant treatment effect from receiving real-time information. Second, we find that learning plays a more prominent role than saliency in driving energy conservation. Our findings support the use of energy conservation programs that target consumer knowledge regarding the energy use of different devices and activities.

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided.

Energy Usage Behavior Modeling in Energy Disaggregation via Marked Hawkes Process

Energy disaggregation, the task of taking a whole home electricity signal and decomposing it into its component appliances, has been proved to be essential in energy conservation research. One powerful cue for breaking down the entire household's energy consumption is user's daily energy usage behavior, which has so far received little attention: existing works on energy disaggregation mostly ignored the relationship between the energy usages of various appliances across different time slots. To model such relationship, we combine topic models with Hawkes processes, and propose a novel probabilistic model based on marked Hawkes process that enables the modeling of marked event data. The proposed model seeks to capture the influence from the occurrence and the marks of one usage event to the occurrence and the marks of subsequent usage events in the future. We also develop an inference algorithm based on variational inference for model parameter estimation. Experimental results on both synthetic data and three real world data sets demonstrate the effectiveness of our model, which outperforms state-of-the-art approaches in decomposing the entire consumed energy to each appliance. Analyzing the influence captured by the proposed model provides further insights into numerous interesting energy usage behavior patterns.

Building power demand response methods toward smart grid

Smart grid has been drawing attention particularly when renewable generations are integrated. In order to ensure high power reliability and energy efficiency in an electrical grid, research and application has been conducted at power supply side to solve the grid critical issues: peak load and power imbalance. However, as the major end-users at power demand side, buildings can also play a significant and cost-effective role by making use of their power demand responses. Different demand response programs (e.g., time- and incentive-based) have been developed and applied for encouraging the end-users to change their energy usage behaviors expected by the grid. Generally, buildings are able to limit and/or shift the power demands according to their own considerations under the specific incentives. A comprehensive review on the building power demand response methods is still missing, although research and application has been investigated and conducted on power demand aspects concerning the building system configuration and the control strategies of power demand optimization. This article, therefore, presents a comprehensive review on the strategies, impacts, and benefits of building power demand response in a grid to systematically evaluate and make better use of their demand response potentials. The possibility of developing proper building power demand response strategies for offline and online applications of the smart grid is also discussed.

A Highly Configurable Simulator for Assessing Energy Usage

Smart grids provide newer ways of energy production, transmission, and distribution. In a smart grid finer control of electrical devices in household and buildings are implemented to better manage energy demand and supply. However, this finer control commonly known as demand side management (DSM) requires extensive simulation at various levels before a DSM algorithm may actually be deployed in a real building or neighborhood. Since forecasting the energy usage behavior of myriad number of electrical devices is a difficult exercise, simulations are done to assess the effectiveness of a DSM algorithm. The problem with the state-of-the-art simulators is that each is designed for simulating electrical devices’ behavior under specific and limited settings. To this end, we present a highly configurable and extensible smart grid simulator (SGS) that is capable of simulating per-minute granularity of energy usage under numerous settings. Moreover, SGS is able to simulate behavior at four levels: electrical devices, households and buildings, neighborhoods and cities. Given different scenarios SGS can simulate relativistic behavior of energy usage at all four levels.

Evaluation of the “Lose Your Excuse” Public Service Advertising Campaign for Tweens to Save Energy

This study evaluates the 2008-2009 "Lose your Excuse" public service advertising (PSA) campaign on energy efficiency targeting 8- to 12-year-olds, intended to increase knowledge, foster proactive attitudes, and change energy usage behaviors. Baseline and two follow-up surveys were conducted with online samples representative of the national population of households with kids with online access. Almost half (47%) of the tweens recognized at least one ad from the campaign. Ad recognition was positively associated with knowledge, proactive attitudes, and energy-saving behavior. Propensity score analysis confirmed a small but measurable and statistically significant effect on energy-saving behavior. The discussion section compares these results to public health campaigns in terms of ghost awareness, reach, and effect size.